假新聞背后的嚴(yán)峻現(xiàn)實(shí)

“Our goal is to build the perfect personalised newspaper for every person in the world,” said Facebook’s Mark Zuckerberg in 2014. This newspaper would “show you the stuff that’s going to be most interesting to you”.

2014年，F(xiàn)acebook的馬克•扎克伯格(Mark Zuckerberg)曾表示：“我們的目標(biāo)是為世界上每個(gè)人打造完美的個(gè)性化報(bào)紙。”這份報(bào)紙將“讓你看到最令你感興趣的內(nèi)容”。

To many, that statement explains perfectly why Facebook is such a terrible source of news.

在許多人看來，這番話完美說明了為什么Facebook是一個(gè)如此糟糕的新聞來源。

A “fake news” story proclaiming that Pope Francis had endorsed Donald Trump was, according to an analysis from BuzzFeed, the single most successful item of news on Facebook in the three months before the US election. If that’s what the site’s algorithms decide is interesting, it’s far from being a “perfect newspaper”.

BuzzFeed的一項(xiàng)分析顯示，美國(guó)大選前的3個(gè)月，F(xiàn)acebook上最熱門的單條新聞是一則宣稱教皇方濟(jì)各(Pope Francis)已表示支持唐納德•特朗普(Donald Trump)的“假新聞”。如果這就是該網(wǎng)站算法選定的令人感興趣的內(nèi)容，那么它根本稱不上是一份“完美的報(bào)紙”。

It’s no wonder that Zuckerberg found himself on the back foot after Trump’s election. Shortly after his victory, Zuckerberg declared: “I think the idea that fake news on Facebook, which is a very small amount of the content, influenced the election in any way . . . is a pretty crazy idea.” His comment was greeted with a scornful response.

難怪扎克伯格在特朗普當(dāng)選后發(fā)現(xiàn)自己陷入了不利境地。特朗普勝選后不久，扎克伯格就宣布：“有人認(rèn)為Facebook上的假新聞——只占內(nèi)容極小一部分——多少影響了大選……我覺得這是一個(gè)相當(dāng)愚蠢的想法。”他此番評(píng)論遭到了公眾的嘲諷。

I should confess my own biases here. I despise Facebook for all the reasons people usually despise Facebook (privacy, market power, distraction, fake-smile social interactions and the rest). And, as a loyal FT columnist, I need hardly point out that the perfect newspaper is the one you’re reading right now.

在此，我應(yīng)該坦白自己心中的偏見。人們常常因隱私權(quán)、市場(chǎng)支配力、分散注意力、假笑社交等原因瞧不上Facebook，這些也都是我瞧不上Facebook的原因。而且，作為英國(guó)《金融時(shí)報(bào)》忠誠(chéng)的專欄作家，我?guī)缀醪恍枰赋?，您此刻正在讀的就是一份完美的報(bào)紙。

But, despite this, I’m going to stand up for Zuckerberg, who recently posted a 5,700-word essay defending social media. What he says in the essay feels like it must be wrong. But the data suggest that he’s right. Fake news can stoke isolated incidents of hatred and violence. But neither fake news nor the algorithmically driven “filter bubble” is a major force in the overall media landscape. Not yet.

但即便如此，我還是要支持扎克伯格，他最近發(fā)表了一篇5700字的文章為社交媒體辯護(hù)。他在文章里講的給人的第一感覺是，他一定講錯(cuò)了，但文中數(shù)據(jù)表明他是對(duì)的。假新聞可以激起個(gè)別的仇恨和暴力事件。但在整個(gè)媒體版圖中，無論是假新聞，還是算法驅(qū)動(dòng)的“過濾氣泡”(filter bubble)，都并非主要力量——至少暫時(shí)不是。

“Fake news” is a phrase that has already been debased. A useful definition is that fake news is an entirely fabricated report presenting itself as a news story. This excludes biased reporting, satire and lies from politicians themselves.

“假新聞”本就是一個(gè)貶義詞。一個(gè)貼切的定義是：假新聞是一種將自身包裝為新聞故事的完全捏造的報(bào)道。這排除了偏見報(bào)道、諷刺作品和政客們的謊言。

At first glance, such hoaxes appear to be ubiquitous on Facebook. The BuzzFeed analysis finds that the five most popular hoax stories were more successful than the five most popular true stories. (This list of true stories includes the New York Post’s “Melania Trump’s Girl-on-Girl Photos From Racy Shoot Revealed”, a reminder that not all mainstream journalism is likely to win a Pulitzer.)

乍看之下，此類假新聞在Facebook上似乎無處不在。BuzzFeed的分析發(fā)現(xiàn)，最熱門的5篇虛假報(bào)道比最熱門的5篇真實(shí)報(bào)道影響力更大。(這些真實(shí)報(bào)道包括《紐約郵報(bào)》(New York Post)刊登的《梅拉尼婭•特朗普(Melania Trump)女女不雅照流出》(Melania Trump’s Girl-on-Girl Photos From Racy Shoot Revealed)，提醒人們不是所有主流新聞都可能贏得普利策獎(jiǎng)(Pulitzer)。)

But hoax stories are less significant than this analysis suggests — partly because Facebook is not the main source of news for Americans (that’s still television news), and partly because true reports will generally be covered in some form by dozens of outlets, which will dilute the popularity of any one version. Each hoax, however, is unique. No wonder the most popular hoaxes outperform the most popular true reports.

但假新聞也并不像上述分析顯示的那么影響重大，部分原因在于Facebook并非美國(guó)人的主要新聞來源(主要來源仍是電視新聞);另一部分原因是，真實(shí)新聞通常被幾十家媒體以不同形式報(bào)導(dǎo)，這將稀釋任一版本報(bào)道的普及度。但每條假新聞都是獨(dú)一無二的。最熱門的真實(shí)報(bào)道敵不過最熱門的假新聞就不足為奇了。

In January 2017, two economists, Hunt Allcott and Matthew Gentzkow, published research studying exactly how prevalent fake news had been before the election. Their clever method tested people’s recall of fake news, as compared with true news stories and “placebo” stories — fake fake news, invented by the researchers. People didn’t remember many fake news stories, and claimed to remember quite a few placebos. Overall, there just didn’t seem to be enough fake news to swing the election result — unless it was potent stuff indeed, even in small doses.

2017年1月，經(jīng)濟(jì)學(xué)家亨特•阿爾科特(Hunt Allcott)與馬修•根茨科(Matthew Gentzkow)發(fā)表了一項(xiàng)針對(duì)大選前假新聞究竟多么泛濫的研究。他們用巧妙的辦法測(cè)試了人們對(duì)假新聞的記憶力，并與真實(shí)新聞報(bào)道和“安慰劑”報(bào)道(兩位研究人員編造的假新聞)進(jìn)行比較。人們并未記住多少假新聞，而且聲稱記住了不少安慰劑報(bào)道?？偠灾坪鯖]有足夠多的假新聞來左右選舉結(jié)果——除非內(nèi)容的確勁爆，即便劑量很小。

“The average voter saw one fake news story before the election,” Gentzkow told me. “That number is a very different picture from what you might get from watching the public discussion.”

根茨科告訴我：“大選前，平均每個(gè)選民會(huì)看到一條假新聞報(bào)道。這一數(shù)字可能與你從公共討論中得到的印象大相徑庭。”

Of more concern is that Facebook — and its “most interesting to you” algorithm — simply supplies news that panders to each user’s ideological biases. It’s undoubtedly true that we surround ourselves with people who agree with us on social media. But it’s not clear that Facebook’s algorithm is the biggest problem here. Twitter was politically polarised even in the days when it used no algorithm at all. And newspapers have ideological biases too.

更令人擔(dān)心的是，F(xiàn)acebook(及其“令你最感興趣的”算法)提供了迎合每位用戶意識(shí)形態(tài)偏見的新聞。毫無疑問，我們?cè)谏缃幻襟w上把自己包裹在與我們持相同看法的人群里。但這并不能說明Facebook的算法是這方面的最大問題。即便在沒有使用任何算法的時(shí)候，Twitter在政治上也處于兩極分化。而報(bào)紙同樣也存在意識(shí)形態(tài)偏見。

One recent study of online news reading was conducted by Seth Flaxman, Sharad Goel and Justin Rao, who had access to browser data from Microsoft, and used it to examine how people consumed news online. They found a mixed picture: social media did seem to push stories that were further from the centre of the political spectrum but they also exposed people to a greater variety of ideological viewpoints. That makes sense. Reading the same newspaper every day is a filter bubble too.

塞思•弗拉克斯曼(Seth Flaxman)、沙拉德•戈埃爾(Sharad Goel)和賈斯汀•拉奧(Justin Rao)最近就在線新聞閱讀進(jìn)行了一項(xiàng)研究，他們獲取了微軟(Microsoft)的瀏覽器數(shù)據(jù)，并據(jù)此研究人們?nèi)绾卧诰€閱讀新聞。他們發(fā)現(xiàn)了一個(gè)復(fù)雜現(xiàn)象：社交媒體的確似乎在推送那些距離政治譜系中心較遠(yuǎn)的報(bào)道，但它們也向人們呈現(xiàn)更多樣化的意識(shí)形態(tài)觀點(diǎn)。這很有意義。畢竟，每天閱讀同一份報(bào)紙也是一種過濾氣泡。

Gentzkow studied the contrast between online and offline news using data from 2004-2009, working with fellow economist Jesse Shapiro. They found little evidence then that online news consumption was more polarised than traditional media. But things are changing quickly. “My guess is that segregation is noticeably and meaningfully higher than in the past,” Gentzkow says, “but still quite modest.”

根茨科與經(jīng)濟(jì)學(xué)家同事杰西•夏皮羅(Jesse Shapiro)合作，利用2004至2009年的數(shù)據(jù)對(duì)線上和線下新聞之間的差異進(jìn)行了研究。但他們發(fā)現(xiàn)，幾乎沒有證據(jù)表明在線新聞消費(fèi)比傳統(tǒng)媒體消費(fèi)更加極化。但情況正在飛快變化。“我的猜測(cè)是，人們之間的分隔顯著而切實(shí)地提高了，”根茨科說，“但仍不算嚴(yán)重。”

This feels like an important moment. Fake news is not prevalent, but it could become so. Filter bubbles are probably no worse than they have been for decades — but that could change rapidly too.

現(xiàn)在感覺像是一個(gè)重要時(shí)刻。假新聞還未遍地都是，但或許會(huì)有這么一天。過濾氣泡可能不比過去幾十年更糟，但這種狀況同樣可能迅速改變。

“A lot ultimately hinges on what the motivations of American voters are,” says Gentzkow. “Do people actually care at all about getting the truth and having accurate information?”

“很多事最終取決于美國(guó)選民的動(dòng)機(jī)是什么，”根茨科說，“人們真的那么在乎獲知真相、得到準(zhǔn)確信息嗎?”

He’s hopeful that, deep down, people watch and read the news because they want to learn about the world. But if what voters really want is to be lied to, then Facebook is the least of our problems.

他從心底希望，人們看新聞、讀新聞是因?yàn)樗麄兿肓私膺@個(gè)世界。但如果選民真正想要的是哄騙，那么Facebook最不該成為我們的難題。